Overview

Dataset statistics

Number of variables18
Number of observations17379
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory81.2 B

Variable types

Numeric12
DateTime1
Categorical3
Boolean2

Alerts

instant is highly overall correlated with season and 1 other fieldsHigh correlation
mnth is highly overall correlated with seasonHigh correlation
hr is highly overall correlated with registered and 2 other fieldsHigh correlation
weekday is highly overall correlated with workingdayHigh correlation
temp is highly overall correlated with atemp and 2 other fieldsHigh correlation
atemp is highly overall correlated with temp and 2 other fieldsHigh correlation
casual is highly overall correlated with temp and 4 other fieldsHigh correlation
registered is highly overall correlated with hr and 3 other fieldsHigh correlation
cnt is highly overall correlated with hr and 3 other fieldsHigh correlation
cnt_grouped is highly overall correlated with hr and 3 other fieldsHigh correlation
season is highly overall correlated with instant and 3 other fieldsHigh correlation
yr is highly overall correlated with instantHigh correlation
workingday is highly overall correlated with weekdayHigh correlation
holiday is highly imbalanced (81.2%)Imbalance
instant is uniformly distributedUniform
instant has unique valuesUnique
hr has 726 (4.2%) zerosZeros
weekday has 2502 (14.4%) zerosZeros
windspeed has 2180 (12.5%) zerosZeros
casual has 1581 (9.1%) zerosZeros
cnt_grouped has 3964 (22.8%) zerosZeros

Reproduction

Analysis started2023-03-03 14:09:11.905640
Analysis finished2023-03-03 14:09:21.349889
Duration9.44 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

instant
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct17379
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8690
Minimum1
Maximum17379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:21.407748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile869.9
Q14345.5
median8690
Q313034.5
95-th percentile16510.1
Maximum17379
Range17378
Interquartile range (IQR)8689

Descriptive statistics

Standard deviation5017.0295
Coefficient of variation (CV)0.57733366
Kurtosis-1.2
Mean8690
Median Absolute Deviation (MAD)4345
Skewness0
Sum1.5102351 × 108
Variance25170585
MonotonicityStrictly increasing
2023-03-03T15:09:21.472993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
11592 1
 
< 0.1%
11578 1
 
< 0.1%
11579 1
 
< 0.1%
11580 1
 
< 0.1%
11581 1
 
< 0.1%
11582 1
 
< 0.1%
11583 1
 
< 0.1%
11584 1
 
< 0.1%
11585 1
 
< 0.1%
Other values (17369) 17369
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
17379 1
< 0.1%
17378 1
< 0.1%
17377 1
< 0.1%
17376 1
< 0.1%
17375 1
< 0.1%
17374 1
< 0.1%
17373 1
< 0.1%
17372 1
< 0.1%
17371 1
< 0.1%
17370 1
< 0.1%

dteday
Date

Distinct731
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size135.9 KiB
Minimum2011-01-01 00:00:00
Maximum2012-12-31 00:00:00
2023-03-03T15:09:21.534400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:21.589589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

season
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.3 KiB
3
4496 
2
4409 
1
4242 
4
4232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 4496
25.9%
2 4409
25.4%
1 4242
24.4%
4 4232
24.4%

Length

2023-03-03T15:09:21.638942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-03T15:09:21.686418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 4496
25.9%
2 4409
25.4%
1 4242
24.4%
4 4232
24.4%

Most occurring characters

ValueCountFrequency (%)
3 4496
25.9%
2 4409
25.4%
1 4242
24.4%
4 4232
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17379
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 4496
25.9%
2 4409
25.4%
1 4242
24.4%
4 4232
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common 17379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 4496
25.9%
2 4409
25.4%
1 4242
24.4%
4 4232
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 4496
25.9%
2 4409
25.4%
1 4242
24.4%
4 4232
24.4%

yr
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
1
8734 
0
8645 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 8734
50.3%
0 8645
49.7%

Length

2023-03-03T15:09:21.826482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-03T15:09:22.011129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 8734
50.3%
0 8645
49.7%

Most occurring characters

ValueCountFrequency (%)
1 8734
50.3%
0 8645
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17379
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8734
50.3%
0 8645
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common 17379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8734
50.3%
0 8645
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8734
50.3%
0 8645
49.7%

mnth
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5377755
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:22.063187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4387757
Coefficient of variation (CV)0.52598559
Kurtosis-1.2018782
Mean6.5377755
Median Absolute Deviation (MAD)3
Skewness-0.0092532484
Sum113620
Variance11.825178
MonotonicityNot monotonic
2023-03-03T15:09:22.155639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 1488
8.6%
7 1488
8.6%
12 1483
8.5%
8 1475
8.5%
3 1473
8.5%
10 1451
8.3%
6 1440
8.3%
4 1437
8.3%
9 1437
8.3%
11 1437
8.3%
Other values (2) 2770
15.9%
ValueCountFrequency (%)
1 1429
8.2%
2 1341
7.7%
3 1473
8.5%
4 1437
8.3%
5 1488
8.6%
6 1440
8.3%
7 1488
8.6%
8 1475
8.5%
9 1437
8.3%
10 1451
8.3%
ValueCountFrequency (%)
12 1483
8.5%
11 1437
8.3%
10 1451
8.3%
9 1437
8.3%
8 1475
8.5%
7 1488
8.6%
6 1440
8.3%
5 1488
8.6%
4 1437
8.3%
3 1473
8.5%

hr
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.546752
Minimum0
Maximum23
Zeros726
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:22.232086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9144051
Coefficient of variation (CV)0.5988182
Kurtosis-1.1980206
Mean11.546752
Median Absolute Deviation (MAD)6
Skewness-0.01067991
Sum200671
Variance47.808998
MonotonicityNot monotonic
2023-03-03T15:09:22.276493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
17 730
 
4.2%
16 730
 
4.2%
13 729
 
4.2%
15 729
 
4.2%
14 729
 
4.2%
12 728
 
4.2%
22 728
 
4.2%
21 728
 
4.2%
20 728
 
4.2%
19 728
 
4.2%
Other values (14) 10092
58.1%
ValueCountFrequency (%)
0 726
4.2%
1 724
4.2%
2 715
4.1%
3 697
4.0%
4 697
4.0%
5 717
4.1%
6 725
4.2%
7 727
4.2%
8 727
4.2%
9 727
4.2%
ValueCountFrequency (%)
23 728
4.2%
22 728
4.2%
21 728
4.2%
20 728
4.2%
19 728
4.2%
18 728
4.2%
17 730
4.2%
16 730
4.2%
15 729
4.2%
14 729
4.2%

holiday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
False
16879 
True
 
500
ValueCountFrequency (%)
False 16879
97.1%
True 500
 
2.9%
2023-03-03T15:09:22.323862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

weekday
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0036826
Minimum0
Maximum6
Zeros2502
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:22.355475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0057715
Coefficient of variation (CV)0.66777077
Kurtosis-1.2559969
Mean3.0036826
Median Absolute Deviation (MAD)2
Skewness-0.0029982214
Sum52201
Variance4.0231191
MonotonicityNot monotonic
2023-03-03T15:09:22.388644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 2512
14.5%
0 2502
14.4%
5 2487
14.3%
1 2479
14.3%
3 2475
14.2%
4 2471
14.2%
2 2453
14.1%
ValueCountFrequency (%)
0 2502
14.4%
1 2479
14.3%
2 2453
14.1%
3 2475
14.2%
4 2471
14.2%
5 2487
14.3%
6 2512
14.5%
ValueCountFrequency (%)
6 2512
14.5%
5 2487
14.3%
4 2471
14.2%
3 2475
14.2%
2 2453
14.1%
1 2479
14.3%
0 2502
14.4%

workingday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
True
11865 
False
5514 
ValueCountFrequency (%)
True 11865
68.3%
False 5514
31.7%
2023-03-03T15:09:22.434056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

weathersit
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.3 KiB
1
11413 
2
4544 
3
1419 
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17379
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 11413
65.7%
2 4544
 
26.1%
3 1419
 
8.2%
4 3
 
< 0.1%

Length

2023-03-03T15:09:22.471700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-03T15:09:22.519656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 11413
65.7%
2 4544
 
26.1%
3 1419
 
8.2%
4 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 11413
65.7%
2 4544
 
26.1%
3 1419
 
8.2%
4 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17379
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11413
65.7%
2 4544
 
26.1%
3 1419
 
8.2%
4 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 17379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11413
65.7%
2 4544
 
26.1%
3 1419
 
8.2%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11413
65.7%
2 4544
 
26.1%
3 1419
 
8.2%
4 3
 
< 0.1%

temp
Real number (ℝ)

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49698717
Minimum0.02
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:22.568133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.2
Q10.34
median0.5
Q30.66
95-th percentile0.8
Maximum1
Range0.98
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.19255612
Coefficient of variation (CV)0.38744687
Kurtosis-0.9418442
Mean0.49698717
Median Absolute Deviation (MAD)0.16
Skewness-0.0060208833
Sum8637.14
Variance0.03707786
MonotonicityNot monotonic
2023-03-03T15:09:22.622635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62 726
 
4.2%
0.66 693
 
4.0%
0.64 692
 
4.0%
0.7 690
 
4.0%
0.6 675
 
3.9%
0.36 671
 
3.9%
0.34 645
 
3.7%
0.3 641
 
3.7%
0.4 614
 
3.5%
0.32 611
 
3.5%
Other values (40) 10721
61.7%
ValueCountFrequency (%)
0.02 17
 
0.1%
0.04 16
 
0.1%
0.06 16
 
0.1%
0.08 17
 
0.1%
0.1 51
 
0.3%
0.12 76
 
0.4%
0.14 138
 
0.8%
0.16 230
1.3%
0.18 155
0.9%
0.2 354
2.0%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.98 1
 
< 0.1%
0.96 16
 
0.1%
0.94 17
 
0.1%
0.92 49
 
0.3%
0.9 90
0.5%
0.88 53
 
0.3%
0.86 131
0.8%
0.84 138
0.8%
0.82 213
1.2%

atemp
Real number (ℝ)

Distinct65
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4757751
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:22.677706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2121
Q10.3333
median0.4848
Q30.6212
95-th percentile0.7424
Maximum1
Range1
Interquartile range (IQR)0.2879

Descriptive statistics

Standard deviation0.17185022
Coefficient of variation (CV)0.36120052
Kurtosis-0.84541189
Mean0.4757751
Median Absolute Deviation (MAD)0.1364
Skewness-0.090428859
Sum8268.4955
Variance0.029532497
MonotonicityNot monotonic
2023-03-03T15:09:22.732179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6212 988
 
5.7%
0.5152 618
 
3.6%
0.4091 614
 
3.5%
0.3333 600
 
3.5%
0.6667 593
 
3.4%
0.6061 588
 
3.4%
0.5303 579
 
3.3%
0.5 575
 
3.3%
0.4545 559
 
3.2%
0.303 549
 
3.2%
Other values (55) 11116
64.0%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.0152 4
 
< 0.1%
0.0303 8
 
< 0.1%
0.0455 9
 
0.1%
0.0606 14
 
0.1%
0.0758 28
 
0.2%
0.0909 13
 
0.1%
0.1061 35
 
0.2%
0.1212 86
0.5%
0.1364 90
0.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9848 2
 
< 0.1%
0.9545 1
 
< 0.1%
0.9242 5
 
< 0.1%
0.9091 5
 
< 0.1%
0.8939 15
 
0.1%
0.8788 19
0.1%
0.8636 20
0.1%
0.8485 32
0.2%
0.8333 41
0.2%

hum
Real number (ℝ)

Distinct89
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62722884
Minimum0
Maximum1
Zeros22
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:22.786594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31
Q10.48
median0.63
Q30.78
95-th percentile0.93
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.19292983
Coefficient of variation (CV)0.30759082
Kurtosis-0.82611674
Mean0.62722884
Median Absolute Deviation (MAD)0.15
Skewness-0.11128715
Sum10900.61
Variance0.037221921
MonotonicityNot monotonic
2023-03-03T15:09:22.840346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88 657
 
3.8%
0.83 630
 
3.6%
0.94 560
 
3.2%
0.87 488
 
2.8%
0.7 430
 
2.5%
0.66 388
 
2.2%
0.65 387
 
2.2%
0.69 359
 
2.1%
0.55 352
 
2.0%
0.74 341
 
2.0%
Other values (79) 12787
73.6%
ValueCountFrequency (%)
0 22
0.1%
0.08 1
 
< 0.1%
0.1 1
 
< 0.1%
0.12 1
 
< 0.1%
0.13 1
 
< 0.1%
0.14 2
 
< 0.1%
0.15 4
 
< 0.1%
0.16 10
0.1%
0.17 10
0.1%
0.18 10
0.1%
ValueCountFrequency (%)
1 270
1.6%
0.97 1
 
< 0.1%
0.96 3
 
< 0.1%
0.94 560
3.2%
0.93 331
1.9%
0.92 2
 
< 0.1%
0.91 1
 
< 0.1%
0.9 7
 
< 0.1%
0.89 239
 
1.4%
0.88 657
3.8%

windspeed
Real number (ℝ)

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19009761
Minimum0
Maximum0.8507
Zeros2180
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:22.889098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1045
median0.194
Q30.2537
95-th percentile0.4179
Maximum0.8507
Range0.8507
Interquartile range (IQR)0.1492

Descriptive statistics

Standard deviation0.12234023
Coefficient of variation (CV)0.64356533
Kurtosis0.59082041
Mean0.19009761
Median Absolute Deviation (MAD)0.0895
Skewness0.5749052
Sum3303.7063
Variance0.014967132
MonotonicityNot monotonic
2023-03-03T15:09:22.932172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 2180
12.5%
0.1343 1738
10.0%
0.1642 1695
9.8%
0.194 1657
9.5%
0.1045 1617
9.3%
0.2239 1513
8.7%
0.0896 1425
8.2%
0.2537 1295
7.5%
0.2836 1048
6.0%
0.2985 808
 
4.6%
Other values (20) 2403
13.8%
ValueCountFrequency (%)
0 2180
12.5%
0.0896 1425
8.2%
0.1045 1617
9.3%
0.1343 1738
10.0%
0.1642 1695
9.8%
0.194 1657
9.5%
0.2239 1513
8.7%
0.2537 1295
7.5%
0.2836 1048
6.0%
0.2985 808
 
4.6%
ValueCountFrequency (%)
0.8507 2
 
< 0.1%
0.8358 1
 
< 0.1%
0.806 2
 
< 0.1%
0.7761 1
 
< 0.1%
0.7463 2
 
< 0.1%
0.7164 2
 
< 0.1%
0.6866 5
 
< 0.1%
0.6567 11
0.1%
0.6418 14
0.1%
0.6119 23
0.1%

casual
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct322
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.676218
Minimum0
Maximum367
Zeros1581
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:22.981227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median17
Q348
95-th percentile138.1
Maximum367
Range367
Interquartile range (IQR)44

Descriptive statistics

Standard deviation49.30503
Coefficient of variation (CV)1.3820139
Kurtosis7.5710017
Mean35.676218
Median Absolute Deviation (MAD)16
Skewness2.4992369
Sum620017
Variance2430.986
MonotonicityNot monotonic
2023-03-03T15:09:23.035538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1581
 
9.1%
1 1082
 
6.2%
2 798
 
4.6%
3 697
 
4.0%
4 561
 
3.2%
5 509
 
2.9%
6 448
 
2.6%
7 405
 
2.3%
8 377
 
2.2%
9 348
 
2.0%
Other values (312) 10573
60.8%
ValueCountFrequency (%)
0 1581
9.1%
1 1082
6.2%
2 798
4.6%
3 697
4.0%
4 561
 
3.2%
5 509
 
2.9%
6 448
 
2.6%
7 405
 
2.3%
8 377
 
2.2%
9 348
 
2.0%
ValueCountFrequency (%)
367 1
< 0.1%
362 1
< 0.1%
361 1
< 0.1%
357 1
< 0.1%
356 1
< 0.1%
355 1
< 0.1%
354 1
< 0.1%
352 1
< 0.1%
350 1
< 0.1%
347 1
< 0.1%

registered
Real number (ℝ)

Distinct776
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.78687
Minimum0
Maximum886
Zeros24
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:23.093899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q134
median115
Q3220
95-th percentile465
Maximum886
Range886
Interquartile range (IQR)186

Descriptive statistics

Standard deviation151.35729
Coefficient of variation (CV)0.98420162
Kurtosis2.7500178
Mean153.78687
Median Absolute Deviation (MAD)89
Skewness1.5579042
Sum2672662
Variance22909.028
MonotonicityNot monotonic
2023-03-03T15:09:23.298637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 307
 
1.8%
3 294
 
1.7%
5 287
 
1.7%
6 266
 
1.5%
2 245
 
1.4%
1 201
 
1.2%
7 200
 
1.2%
8 190
 
1.1%
9 178
 
1.0%
11 140
 
0.8%
Other values (766) 15071
86.7%
ValueCountFrequency (%)
0 24
 
0.1%
1 201
1.2%
2 245
1.4%
3 294
1.7%
4 307
1.8%
5 287
1.7%
6 266
1.5%
7 200
1.2%
8 190
1.1%
9 178
1.0%
ValueCountFrequency (%)
886 1
< 0.1%
885 1
< 0.1%
876 2
< 0.1%
871 1
< 0.1%
860 1
< 0.1%
857 2
< 0.1%
839 1
< 0.1%
838 1
< 0.1%
833 1
< 0.1%
822 1
< 0.1%

cnt
Real number (ℝ)

Distinct869
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.46309
Minimum1
Maximum977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:23.351645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q140
median142
Q3281
95-th percentile563.1
Maximum977
Range976
Interquartile range (IQR)241

Descriptive statistics

Standard deviation181.3876
Coefficient of variation (CV)0.95737698
Kurtosis1.4172033
Mean189.46309
Median Absolute Deviation (MAD)112
Skewness1.2774116
Sum3292679
Variance32901.461
MonotonicityNot monotonic
2023-03-03T15:09:23.405059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 260
 
1.5%
6 236
 
1.4%
4 231
 
1.3%
3 224
 
1.3%
2 208
 
1.2%
7 198
 
1.1%
8 182
 
1.0%
1 158
 
0.9%
10 155
 
0.9%
11 147
 
0.8%
Other values (859) 15380
88.5%
ValueCountFrequency (%)
1 158
0.9%
2 208
1.2%
3 224
1.3%
4 231
1.3%
5 260
1.5%
6 236
1.4%
7 198
1.1%
8 182
1.0%
9 128
0.7%
10 155
0.9%
ValueCountFrequency (%)
977 1
< 0.1%
976 1
< 0.1%
970 1
< 0.1%
968 1
< 0.1%
967 1
< 0.1%
963 1
< 0.1%
957 1
< 0.1%
953 1
< 0.1%
948 1
< 0.1%
943 1
< 0.1%

cnt_grouped
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3294206
Minimum0
Maximum29
Zeros3964
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size135.9 KiB
2023-03-03T15:09:23.453303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q38
95-th percentile17
Maximum29
Range29
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.5424946
Coefficient of variation (CV)1.0399807
Kurtosis1.4606921
Mean5.3294206
Median Absolute Deviation (MAD)4
Skewness1.2995036
Sum92620
Variance30.719247
MonotonicityNot monotonic
2023-03-03T15:09:23.495459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 3964
22.8%
1 1652
9.5%
2 1356
 
7.8%
3 1313
 
7.6%
4 1208
 
7.0%
5 1184
 
6.8%
6 1030
 
5.9%
7 870
 
5.0%
8 759
 
4.4%
9 654
 
3.8%
Other values (20) 3389
19.5%
ValueCountFrequency (%)
0 3964
22.8%
1 1652
9.5%
2 1356
 
7.8%
3 1313
 
7.6%
4 1208
 
7.0%
5 1184
 
6.8%
6 1030
 
5.9%
7 870
 
5.0%
8 759
 
4.4%
9 654
 
3.8%
ValueCountFrequency (%)
29 9
 
0.1%
28 7
 
< 0.1%
27 21
 
0.1%
26 34
 
0.2%
25 47
0.3%
24 58
0.3%
23 40
 
0.2%
22 64
0.4%
21 84
0.5%
20 106
0.6%

Interactions

2023-03-03T15:09:20.422930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:12.468530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.167375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:14.093589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.123268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.720782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.292082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.982080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.647577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.271914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.070317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.626321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.483994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:12.560321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.235084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:14.180349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.177214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.776688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.343475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.030856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.699163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.326610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.120549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.673830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.555531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:12.638368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.278557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:14.234068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.226438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.827879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.391190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.083373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.751103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.473745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.165668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.718271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.620677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:12.695493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.323350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:14.299492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.275196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.876145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.436226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.143142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.796340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.569801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.209198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.760677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.691555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:12.742548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.368407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:14.758010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.325058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.924080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.484784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.190876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.847505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.634370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.256871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.805489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.747494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:12.794436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.421982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:14.807751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.375367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.970918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.531107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.239769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.891951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.711946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.301876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.007021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.798863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:12.843010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.495332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:14.855714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.421275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.016633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.575522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.285636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.934987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.769320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.347696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.058056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.847441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:12.907928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.561916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:14.901900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.467364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.062622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.621811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.331258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.985247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.818961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.392581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.115101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.893943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:12.976401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.608682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:14.943872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.516132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.108360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.665218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.375349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.029600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.869695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.437598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.173929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.942846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.026253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.672162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:14.990651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.568916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.157082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.716204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.440876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.087077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.922725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.487238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.238472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.997958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.071830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.749210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.031780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.619454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.198602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.759461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.548507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.155330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.969483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.535667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.295291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:21.064292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.117923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:13.865575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.075808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:15.670835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.245121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:16.927108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:17.598553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:18.208754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.019392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:19.579003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-03T15:09:20.347279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-03T15:09:23.545413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
instantmnthhrweekdaytempatemphumwindspeedcasualregisteredcntcnt_groupedseasonyrholidayworkingdayweathersit
instant1.0000.489-0.0050.0010.1280.1260.007-0.0730.1590.2560.2440.2420.8110.9950.0610.0290.089
mnth0.4891.000-0.0060.0100.1910.1910.160-0.1300.1180.1270.1260.1220.8960.0000.1130.0620.086
hr-0.005-0.0061.000-0.0030.1340.133-0.2790.1400.4770.5110.5110.5030.0000.0000.0000.0000.049
weekday0.0010.010-0.0031.000-0.006-0.013-0.0370.0100.0130.0350.0300.0290.0000.0000.2840.9390.043
temp0.1280.1910.134-0.0061.0000.990-0.055-0.0100.5710.3730.4230.4200.5250.0900.0520.0810.095
atemp0.1260.1910.133-0.0130.9901.000-0.053-0.0410.5700.3730.4230.4200.5080.0820.0520.0750.115
hum0.0070.160-0.279-0.037-0.055-0.0531.000-0.294-0.388-0.338-0.360-0.3620.1340.1270.0390.0500.286
windspeed-0.073-0.1300.1400.010-0.010-0.041-0.2941.0000.1230.1230.1270.1270.1050.0220.0000.0000.050
casual0.1590.1180.4770.0130.5710.570-0.3880.1231.0000.7810.8510.8430.1970.1620.0580.3460.091
registered0.2560.1270.5110.0350.3730.373-0.3380.1230.7811.0000.9890.9820.1480.2730.0510.1710.085
cnt0.2440.1260.5110.0300.4230.423-0.3600.1270.8510.9891.0000.9930.1610.2650.0420.1210.093
cnt_grouped0.2420.1220.5030.0290.4200.420-0.3620.1270.8430.9820.9931.0000.1690.2700.0370.1460.093
season0.8110.8960.0000.0000.5250.5080.1340.1050.1970.1480.1610.1691.0000.0000.0430.0320.055
yr0.9950.0000.0000.0000.0900.0820.1270.0220.1620.2730.2650.2700.0001.0000.0000.0000.030
holiday0.0610.1130.0000.2840.0520.0520.0390.0000.0580.0510.0420.0370.0430.0001.0000.2520.020
workingday0.0290.0620.0000.9390.0810.0750.0500.0000.3460.1710.1210.1460.0320.0000.2521.0000.043
weathersit0.0890.0860.0490.0430.0950.1150.2860.0500.0910.0850.0930.0930.0550.0300.0200.0431.000

Missing values

2023-03-03T15:09:21.162409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-03T15:09:21.278829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcntcnt_grouped
012011-01-011010False6False10.240.28790.810.0000313160
122011-01-011011False6False10.220.27270.800.0000832401
232011-01-011012False6False10.220.27270.800.0000527320
342011-01-011013False6False10.240.28790.750.0000310130
452011-01-011014False6False10.240.28790.750.00000110
562011-01-011015False6False20.240.25760.750.08960110
672011-01-011016False6False10.220.27270.800.00002020
782011-01-011017False6False10.200.25760.860.00001230
892011-01-011018False6False10.240.28790.750.00001780
9102011-01-011019False6False10.320.34850.760.000086140
instantdtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcntcnt_grouped
17369173702012-12-31111214False1True20.280.27270.450.2239621852477
17370173712012-12-31111215False1True20.280.28790.450.1343692463159
17371173722012-12-31111216False1True20.260.25760.480.1940301842146
17372173732012-12-31111217False1True20.260.28790.480.0896141501645
17373173742012-12-31111218False1True20.260.27270.480.1343101121223
17374173752012-12-31111219False1True20.260.25760.600.1642111081193
17375173762012-12-31111220False1True20.260.25760.600.1642881892
17376173772012-12-31111221False1True10.260.25760.600.1642783902
17377173782012-12-31111222False1True10.260.27270.560.13431348611
17378173792012-12-31111223False1True10.260.27270.650.13431237491